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Currently, ReduceLROnPlateau function for CTM has a high hard-coded value for patience. So, whenever you want the LR to decrease during training we need to set the EarlyStopping patience to some value higher than 10, otherwise the train stops before the LR is actually decreased.
This is impractical because, waiting 10 epochs for the LR to decrease is a lot.
On Mon, Aug 28, 2023 at 8:40 AM Diogo Rodrigues ***@***.***> wrote:
- Contextualized Topic Models version: 2.5.0
- Python version: 3.9.16
- Operating System: Ubuntu 20.04.6 LTS
Description
Currently, ReduceLROnPlateau function for CTM has a high hard-coded value
for patience. So, whenever you want the LR to decrease during training we
need to set the EarlyStopping patience to some value higher than 10,
otherwise the train stops before the LR is actually decreased.
This is impractical because, waiting 10 epochs for the LR to decrease is a
lot.
Code line
<https://github.com/MilaNLProc/contextualized-topic-models/blob/5dd3238dd3a49e8a792163b02d4c3f3b5deaae9f/contextualized_topic_models/models/ctm.py#L154C13-L154C76>
Propose to fix it
class CTM:
# ...
def __init__(
self,
# ...
reduce_on_plateau=False,
lr_patience: int = 3
# ...
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Description
Currently,
ReduceLROnPlateau
function for CTM has a high hard-coded value forpatience
. So, whenever you want the LR to decrease during training we need to set theEarlyStopping
patience to some value higher than10
, otherwise the train stops before the LR is actually decreased.This is impractical because, waiting 10 epochs for the LR to decrease is a lot.
Code line
Propose to fix it
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